DBMS > DataFS vs. Linter vs. Solr vs. SQLite vs. TinkerGraph

System Properties Comparison DataFS vs. Linter vs. Solr vs. SQLite vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

RDBMS for high security requirements

A widely used distributed, scalable search engine based on Apache Lucene

Widely used embeddable, in-process RDBMS

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Secondary database models

Graph DBMS

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

dev.mobiland.com/Overview.xsp

solr.apache.org/resources.html

www.sqlite.org/docs.html

Developer

Mobiland AG

relex.ru

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2018

1990

2006

2000

2009

Current release

1.1.263, October 2022

9.6.0, April 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Public Domain

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

Java

Server operating systems

Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

server-less

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

SQL-92 is not fully supported

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

TinkerPop 3

Supported programming languages

.Net
C
C#
C++
VB.Net

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Groovy
Java

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

Java plugins

Triggers

no, except callback-events from server when changes happened

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

optional

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

yes

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